The large-scale multiple-input-multiple-output (MIMO), also known as massive MIMO, is one of the key techniques for the fifth-generation (5 G) mobile communications. Due to large-scale antenna systems equipped at the basestations, the user-basestation distance in massive MIMO systems may be within the so-called Rayleigh distance. This would cause challenges in developing algorithms for user localization, because both near-field (NF) and far-field (FF) sources (users) may coexist. To solve this problem, most of existing algorithms are based on a simplified source-sensor spatial model, where the sensor-magnitude is assumed to be equal and the spatial phase is approximated by the Taylor polynomial. In contrast, a new algorithm based on the exact spatial geometry is developed, where no model simplification is made. The new algorithm is termed as MIxed Localization using the Exact model (MILE) in that it sets up a unified (non-approximation) model framework to the problem under consideration, and solves this problem in a mathematically quite simple manner. In fact, the MILE has the following three important advantages: (1) it is not restricted to exploit equally spaced arrays, (2) it can accommodate any arbitrary propagation loss, and (3) it does not suffer the model mismatch caused performance loss. All these advantages are not offered by current state-of-the-art techniques. The matlab codes for replication of the results in this study are available at: https://github.com/jinhesjtu/MILE.git.

The problem of passive localization of mixed near-field (NF) and far-field (FF) source signals in the presence of array gain-phase uncertainties is addressed. A new algorithm is aimed to use partly calibrated nonuniform linear arrays (NLAs), in which only three sensors have been fully-calibrated. Most of the existing algorithms deal with this problem by exploiting uniform linear arrays (ULAs). Moreover, they assume a simplified source-array model, in which the propagation magnitude scaling is completely neglected and the spatial phase difference is approximated by Taylor’s polynomial. As an opposite, the proposed algorithm is employed to accommodate a more general situation: the exact spatial geometries and nonuniform linear arrays. In the proposed algorithm, three cumulant matrices are firstly defined to construct two matrix pencils. Unambiguous range and angle parameter estimates of the NF sources are then obtained from the generalized eigenvalues of the two defined matrix pencils. After that, these estimates are utilized to calibrate array gain-phase errors. Finally, a spectrum-MUSIC like approach is applied to accomplish the angle estimation for the FF sources. The new algorithm is shown to be readily simple and effective and will be verified both mathematically and numerically.

In sensor array processing, the minimum sensor/element-spacing is usually restricted within a half-wavelength in order to avoid estimation aliasing. This would cause sensor/element mutual coupling when antenna-electromagnetics are considered. Based on the recently proposed nested array (NA) concept, we present a new type of NA called sparse nested spatially spread orthogonal dipole array (SNODA) to realize underdetermined direction finding with less mutual coupling. In SNODA, the sensor/element-spacing can be extended to be much higher than a half-wavelength so that the effect of sensor coupling will be much reduced. The direction estimation ambiguities caused by aperture extension is resolved by combining both spatial and element responses of spread orthogonal dipoles. Matlab code for reproduction of the results is available at https://github.com/jinhesjtu/SNODA-DF.git.

This article presents a new method for 3-D localization of an acoustic source signal by estimating its azimuth-angle, elevation-angle, and radial range. The proposed method exploits a spatially spread acoustic vector sensor, which is composed of a tri-axial velocity vector sensor and an isotropic pressure sensor. Unlike the spatially collocated case, the self-normalization of spatially spread acoustic vector sensor’s response is no longer independent of the source location, resulting in the inapplicability of the widely used “self-normalization” estimators (A. Nehorai et al. , 1994), (V. N. Hari et al. , 2012), (Y. Song et al. , 2013). The present article considers the near-field propagation’s path attenuation and phase difference among the sensor components and derives a source location estimation method without resorting to the “self-normalization” operation. Also, the proposed method is applicable to any nonfree-space propagation models at arbitrarily unknown path-loss exponent. In comparison with the existing methods, where the velocity vector sensor is spatially collocated, the proposed method can offer improved performance estimation due to the inherent extension of the vector sensor’s spatial aperture in the spread structure.

In acoustic sensing systems, acoustic vector sensor (also known as vector hydrophone for underwater applications) arrays are widely used. Most of the acoustic vector sensor array signal processing methods presume the minimum spacing between two adjacent sensors or sensor components to be within a half-wavelength in order to avoid azimuth-elevation angle estimation aliasing. This would limit the effective array aperture, thereby reducing the potential estimation accuracy. Furthermore, they are unapplicable to the underdetermined scenarios, where the number of sources exceeds that of sensor components. Exploiting the recently proposed nested array concept, we present a new type of nested array, termed as Sparse Nested spatially spread Square Acoustic Vector sensor Array (SNSAVA) to realize underdetermined 2-D direction finding with increased estimation accuracy. In SNSAVA, interspacing of two sensors and two components of a sensor can be spread to be much higher than a half-wavelength so that the effective array aperture will be significantly extended. An unambiguous angle estimation method is further derived to make this fully sparse array configuration practically feasible. Performance studies focused on underdetermined high-accuracy azimuth-elevation angle estimation are provided via numerical examples. The estimation performance for the SNSAVA is also compared with that of the nested acoustic vector sensor arrays, proposed in [33], and with the Cramér-Rao bound.